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Statistical entropy of resources using a categorization tree for material enumeration : framework development and application to a plastic packaging case study

Journal Contribution - Journal Article

Statistical entropy has been proposed as a metric of the quality of a mixture of materials, capturing its degree of complexity. An often overlooked challenge to applying this metric lies in the selection of material categories (e.g. metals and plastics vs. steel, aluminum, polyethylene, U+2026) considered when evaluating the mixing complexity of a resource. The difficulty becomes particularly apparent when applying the metric to material mixtures from divergent origins or with very different make-up. This work develops an approach using statistical entropy with a multi-level material categorization scheme that is broadly applicable to different types of resources across all life cycle phases. In a case study, material mixing complexity is tracked across fourteen material mixtures found through the life cycle of a typical plastic packaging product. This includes the waste packaging bin, where other materials are embedded, and two end-of-life treatment scenarios: mechanical recycling and pyrolysis, where the waste product is chemically converted. A sharp deterioration of material quality is observed immediately after the productU+2019s use phase. Chemical and mechanical recycling show similar performance by the final stage of the life cycle, when secondary resources are produced. However, to reach this point chemical recycling shows multiple peaks of highly mixed resources while mechanical recycling shows a continuous decrease in mixing complexity throughout end-of-life. This approach presents a flexible method to characterize material mixing complexity as a resource quality indicator, enabling comparisons between disparate resources across typical industry- or use-boundaries, and potentially supporting decisions in product design and end-of-life treatment.
ISSN: 1879-0658
Volume: 181
Number of pages: 1
Publication year:2022